ArticleJournal of chemical theory and computation2019
Efficient Ensemble Refinement by Reweighting.
Article in Journal of chemical theory and computation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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45 citing papers in PubMed.
- A Bayesian Approach to Interpret Time-Resolved Experiments Using Molecular Simulations.ACS physical chemistry Au · 2026Article
- IDPForge: Deep Learning of Proteins with Global and Local Regions of Disorder.bioRxiv : the preprint server for biology · 2026Article
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- Determining accurate conformational ensembles of intrinsically disordered proteins at atomic resolution.Nature communications · 2025Article
- Structural and Functional Relevance of Charge-Based Transient Interactions inside Intrinsically Disordered Proteins.ACS physical chemistry Au · 2025Article
- Combining simulations and experiments - a perspective on maximum entropy methods.Physical chemistry chemical physics : PCCP · 2025Review
- Local structure propensities in disordered proteins from cross-correlated NMR spin relaxation.Journal of biomolecular NMR · 2025Article
- Frontiers in integrative structural modeling of macromolecular assemblies.QRB discovery · 2025Review
- Structure of the aminoterminal domain of the birnaviral multifunctional VP3 protein and its unexplored critical role.PNAS nexus · 2024Article
- Structural and Functional Relevance of Charge Based Transient Interactions inside Intrinsically Disordered Proteins.bioRxiv : the preprint server for biology · 2024Article
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- Bayesian reweighting of biomolecular structural ensembles using heterogeneous cryo-EM maps with the cryoENsemble method.Scientific reports · 2024Article
- Hierarchical Assembly of Single-Stranded RNA.Journal of chemical theory and computation · 2024Article
- FRETpredict: a Python package for FRET efficiency predictions using rotamer libraries.Communications biology · 2024Article
- Representing structures of the multiple conformational states of proteins.Current opinion in structural biology · 2023Review
- Driving forces behind phase separation of the carboxy-terminal domain of RNA polymerase II.Nature communications · 2023Article
- Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data.The Journal of chemical physics · 2023Article
- BICePs v2.0: Software for Ensemble Reweighting Using Bayesian Inference of Conformational Populations.Journal of chemical information and modeling · 2023Article
- Interpreting Transient Interactions of Intrinsically Disordered Proteins.The journal of physical chemistry. B · 2023Article
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6 authors.
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Abstract
Ensemble refinement produces structural ensembles of flexible and dynamic biomolecules by integrating experimental data and molecular simulations. Here we present two efficient numerical methods to solve the computationally challenging maximum-entropy problem arising from a Bayesian formulation of ensemble refinement. Recasting the resulting constrained weight optimization problem into an unconstrained form enables the use of gradient-based algorithms. In two complementary formulations that differ in their dimensionality, we optimize either the log-weights directly or the generalized forces appearing in the explicit analytical form of the solution. We first demonstrate the robustness, accuracy, and efficiency of the two methods using synthetic data. We then use NMR J-couplings to reweight an all-atom molecular dynamics simulation ensemble of the disordered peptide Ala-5 simulated with the AMBER99SB*-ildn-q force field. After reweighting, we find a consistent increase in the population of the polyproline-II conformations and a decrease of α-helical-like conformations. Ensemble refinement makes it possible to infer detailed structural models for biomolecules exhibiting significant dynamics, such as intrinsically disordered proteins, by combining input from experiment and simulation in a balanced manner.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.